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Which type of statistical uncertainty -- statistical (in)significance with ap-value, or a Bayesian probability -- enables people to see the continuousnature of uncertainty more clearly in a policymaking context? An originalsurvey experiment asked participants from Ireland to read a hypotheticalpolicymaking scenario and decide to or not to introduce a new bus line as apolicy to reduce traffic jams, given a research report estimating itseffectiveness. The four types of information were given as the treatments:statistical significance with a p-value of 2%, statistical insignificance witha p-value of 25%, a 95% probability that the estimate is correct, and a 68%probability that the estimate is correct. The effect size and cost of thepolicy were fixed across all treatment groups. In the case of loweruncertainty, both significance and Bayesian frameworks resulted in a largeproportion of participants adopting the policy (.82 and .91 respectively),while in the case of higher uncertainty, the significance framework led a muchsmaller proportion of participants to adopt the policy than the Bayesianframework (.39 against .83). The findings suggest participants were able to seethe continuous nature of uncertainty more clearly in the Bayesian frameworkthan in the significance framework.

Which Type of Statistical Uncertainty Helps Evidence-Based Policymaking? An Insight from a Survey Experiment in Ireland